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| Section | Objectives |
|---|---|
| Implement End-to-End Machine Learning Lifecycle | - Prepare and manage datasets - Build, train, and evaluate machine learning models - Automate machine learning workflows and pipelines - Deploy models and consume model endpoints - Save and manage models using Model Catalog |
| OCI Data Science - Introduction and Configuration | - Use OCI Data Science notebooks and sessions - Configure and manage Data Science resources - Understand OCI Data Science service concepts and architecture |
| Use Related OCI Services | - Design machine learning solutions for business use cases - Integrate OCI Data and AI services - Apply OCI services for data ingestion, storage, and processing |
| Design and Set Up Data Science Workspace | - Manage notebook sessions and compute resources - Create and configure Data Science projects - Use Accelerated Data Science SDK and open source tools |
| Apply MLOps Practices | - Monitor and maintain machine learning models - Implement model lifecycle management - Use best practices for operationalizing ML solutions |
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NEW QUESTION # 156
Which technique can be used for feature engineering in the machine learning lifecycle?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify a feature engineering technique in ML.
Understand Feature Engineering: Transforms raw data into model-ready features.
Evaluate Options:
A . PCA: Reduces dimensionality—feature engineering—correct.
B . K-means: Clustering model—not feature engineering.
C . SVM: Classification model—not feature engineering.
D . Gradient boosting: Model training—not feature engineering.
Reasoning: PCA creates new features via transformation—fits definition.
Conclusion: A is correct.
OCI documentation states: “Feature engineering techniques like Principal Component Analysis (PCA) (A) transform data into new features to enhance model performance.” B, C, and D are modeling techniques—only A aligns with OCI’s feature engineering stage.
1: Oracle Cloud Infrastructure Data Science Documentation, "Feature Engineering Techniques".
NEW QUESTION # 157
Which Oracle Accelerated Data Science (ADS) classes can be used for easy access to datasets fromreference libraries and index websites, such as scikit-learn?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify ADS class for accessing datasets (e.g., scikit-learn).
Evaluate Options:
A: DatasetBrowser—Not an ADS class.
B: DatasetFactory—Loads datasets from sources like scikit-learn—correct.
C: ADSTuner—Hyperparameter tuning, not data access.
D: SecretKeeper—Manages credentials, not datasets.
Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()).
Conclusion: B is correct.
OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit-learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets—only B fits.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory".
NEW QUESTION # 158
Which of the following programming languages are most widely used by data scientists?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify top languages for data science.
Evaluate Options:
A: C/C++—Low-level, less common for data tasks.
B: Python (ML, libraries), R (stats), SQL (data)—Industry standards.
C: Java (enterprise), JavaScript (web)—Not data-focused.
Reasoning: B aligns with data science tools (e.g., pandas, ggplot).
Conclusion: B is correct.
OCI documentation highlights “Python, R, and SQL as the most widely used languages in Data Science for modeling, analysis, and data querying.” C/C++ (A) and Java/JS (C) are less prevalent—B matches OCI’s notebook support and industry trends.
1: Oracle Cloud Infrastructure Data Science Documentation, "Supported Languages".
NEW QUESTION # 159
What do you use the score.py file for?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Determine the purpose of score.py in OCI Data Science model deployment.
Understand Model Deployment: When deploying a model in OCI, artifacts include score.py, runtime.yaml, etc.
Evaluate Options:
A: Infrastructure configuration (e.g., compute shape) is handled by deployment settings, not score.py.
B: score.py contains the inference logic (e.g., load_model(), predict())—correct.
C: Conda environment is defined in runtime.yaml or a requirements file—not score.py.
D: Scaling (e.g., instance count) is set in deployment configuration—not score.py.
Reasoning: score.py is the script executed by the deployment endpoint to load the model and make predictions.
Conclusion: B is the correct purpose.
The OCI Data Science documentation states: “The score.py file is a required artifact for model deployment, containing the inference logic—functions like load_model() to load the model and predict() to generate predictions from input data.” Infrastructure (A) and scaling (D) are managed via the OCI Console or SDK, while the environment (C) is specified in runtime.yaml. B is the precise role of score.py in OCI’s deployment workflow.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - score.py".
NEW QUESTION # 160
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
Evaluate Options:
A: Detailed—Exhaustive, high cost—incorrect.
B: No search space—False; tuning requires a space.
C: Perfunctory—Quick, low-cost assessment—correct.
D: Dictionary—Defines space but not a strategy.
Reasoning: Perfunctory balances cost and initial quality check.
Conclusion: C is correct.
OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 161
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